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Computers and especially computer networks have become an important part of our everyday life. Almost every device we use is equipped with a computer or microcontroller. Recent technology has even boosted this development by miniaturization of the size of microcontrollers. These are used to either process or collect data. Miniature senors may sense and collect huge amounts of information coming from nature, either from environment or from our own bodies. To process and distribute the data of these sensors, wireless sensor networks (WSN) have been developed in the last couple of years. Several microcontrollers are connected over a wireless connection and are able to collect, transmit and process data for various applications. Today, there are several WSN applications available, such as environment monitoring, rescue operations, habitat monitoring and smart home applications. The research group of Prof. Elaine Lawrence at the University of Technology, Sydney (UTS) is focusing on mobile health care with WSN. Small sensors are used to collect vital data. This data is sent over the network to be processed at a central device such as computer, laptop or handheld device. The research group has developed several prototypes of mobile health care. This thesis will deal with enhancing and improving the latest prototype based on CodeBlue, a hardware and software framework for medical care.
The automatic detection of position and orientation of subsea cables and pipelines in camera images enables underwater vehicles to make autonomous inspections. Plants like algae growing on top and nearby cables and pipelines however complicate their visual detection: the determination of the position via border detection followed by line extraction often fails. Probabilistic approaches are here superior to deterministic approaches. Through modeling probabilities it is possible to make assumptions on the state of the system even if the number of extracted features is small. This work introduces a new tracking system for cable/pipeline following in image sequences which is based on particle filters. Extensive experiments on realistic underwater videos show robustness and performance of this approach and demonstrate advantages over previous works.